POS0920 QUANTIFICATION OF TENOSYNOVITIS IN RA FROM WRIST MRIs, BASED ON DEEP LEARNING
Authors: D. Shamonin, Y. LI, T. Hassanzadeh, M. E. Bakker, M. Reijnierse, A. Van der Helm – van Mil, B. Stoel
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> To assess tenosynovitis in wrist MRI scans from patients suspected of developing rheumatoid arthritis (RA), visual scoring (RAMRIS) is usually applied. As this time-consuming and requires rigorous training, we propose a method for the wrist that is based on Deep Learning (DL) combined with post-processing. This method detects all structures (a process called 'segmentation'), including all tendons, and measures inflammation around each tendon (group). <h3>Objectives</h3> To develop a method that automatically segments tendons together with all other anatomical structures of the wrist, and perform automatic tenosynovitis quantification as an alternative to the RAMRIS score. <h3>Methods</h3> Post-contrast axial MR scans of the wrist of 1216 patients with early-onset synovitis from the early arthritis clinic (EAC) cohort were used. From 28 EAC patients, ground truth manual segmentations were obtained as atlases. This dataset contains 33 labels: 14 flexor/extensor tendon groups; 10 carpal, 5 metacarpal bones; 1 label for all main vessels; 2 labels for skin and remaining tissue; and 1 air label. We normalized each input image using the Z-score. For segmentation of the wrist (see Figure 1a), our method utilizes a U-Net. For data augmentations we used intensity scaling (0.8-1.25) and added normally distributed noise (µ= 0, σ=0.06). We used softmax cross-entropy as a loss function, Adam optimization for 200 epochs, with a constant learning rate of 0.001. After 1-fold cross-validation evaluation, we trained on all 28 atlases. For validation we compared the automated tenosynovitis score against the manual RAMRIS score for tenosynovitis. From 10 RAMRIS tenosynovitis scores defined by two clinicians, we computed the mean score (Tµ). To define starting and end MRI slices for the scoring area we followed the RAMRIS definition. Starting from the slice where radius and ulna are closest to each other, ending with the hook of the hamate. The scoring areas were computed 3 mm around tendons, using a Euclidean distance transform, while excluding bones and vessels. Subsequently we computed histograms of the image intensities in the blood vessels and used the 25<sup>th</sup> percentile point as threshold to define inflamed areas within the scoring area (see Figure 1b). The final tenosynovitis quantification (Tq) was defined as the ratio between the number of 'inflamed pixels' and the total scoring area. <h3>Results</h3> In this work, we presented an alternative automatic quantification of tenosynovitis on MRI of the wrist. The results provide sufficient quantitative measurements compared to RAMRIS visual scores. The quantitative measurement was computed for 1216 patients, with Pearson correlation coefficient r = 0.87 and p &lt; 0.001 (see the scatter plot in Figure 1). <h3>Conclusion</h3> Our DL-method provides a way to automate the quantification of tenosynovitis with MRI. It is very fast (~10 sec per patient) as compared to manual visual scoring. We illustrate the cumulative measurement of all synovial areas, while computing per tendon is also feasible. The accomplished results provide a solid basis for automatic tenosynovitis quantification. <h3>Acknowledgements</h3> This AI-project has been funded by the Dutch Research Council (NWO) Applied and Engineering Sciences (project numbers 13329 and 17970). The Dutch Arthritis Society contributed financially to both grants. <h3>Disclosure of Interests</h3> Denis Shamonin Grant/research support from: Bristol-Myers Squibb and Pfizer contributed to this project, through a grant from the Dutch Research Council (NWO), Applied and Engineering Sciences., Yanli Li: None declared, Tahereh Hassanzadeh Grant/research support from: Bristol-Myers Squibb and Pfizer contributed to this project, through a grant from the Dutch Research Council (NWO), Applied and Engineering Sciences., M. Els Bakker: None declared, Monique Reijnierse Grant/research support from: Bristol-Myers Squibb and Pfizer contributed to this project, through a grant from the Dutch Research Council (NWO), Applied and Engineering Sciences., Annette van der Helm – van Mil Grant/research support from: Bristol-Myers Squibb and Pfizer contributed to this project, through a grant from the Dutch Research Council (NWO), Applied and Engineering Sciences., Berend Stoel Grant/research support from: Bristol-Myers Squibb and Pfizer contributed to this project, through a grant from the Dutch Research Council (NWO), Applied and Engineering Sciences.